Life-course influences of poverty on violence and homicide: 30-year Brazilian birth cohort study

Abstract Background Homicide is the leading cause of death among young people in Latin America, one of the world’s most violent regions. Poverty is widely considered a key cause of violence, but theories suggest different effects of poverty, depending on when it is experienced in the life-course. Longitudinal studies of violence are scarce in Latin America, and very few prospective data are available worldwide to test different life-course influences on homicide. Methods In a prospective birth cohort study following 5914 children born in southern Brazil, we examined the role of poverty at birth, in early childhood, and in early adulthood on violence and homicide perpetration, in criminal records up to age 30 years. A novel Structured Life Course Modelling Approach was used to test competing life-course hypotheses about ‘sensitive periods’, ‘accumulation of risk’, and ‘downward mobility’ regarding the influence of poverty on violence and homicide. Results Cumulative poverty and poverty in early adulthood were the most important influences on violence and homicide perpetration. This supports the hypothesis that early adulthood is a sensitive period for the influence of poverty on lethal and non-lethal violence. Results were replicable using different definitions of poverty and an alternative outcome of self-reported fights. Conclusion Cumulative poverty from childhood to adulthood was an important driver of violence and homicide in this population. However, poverty experienced in early adulthood was especially influential, suggesting the importance of proximal mechanisms for violence in this context, such as unemployment, organized crime, drug trafficking, and ineffective policing and justice systems.

As described in the Methods of the main article, of the 5660 cohort participants alive at age 10 years (eligible for this study), successful linkage with crime records was achieved for 5644 participants (99.7%), with 16 unsuccessful record searches or identity doubts.Of these 5627 (99.4% of 5660) had sufficient details in crime records to code whether a violent offence had been committed or not.
Crime records that met the definition of a crime or criminal contravention in Brazil were retained for study, except for abortion.Crimes are considered more serious illegal acts, and evoke a punishment of imprisonment up to 30 years and/or a fine, according to Brazilian law, both for completed and attempted illegal acts.Criminal contraventions are less serious law violations and evoke a simple prison sentence (in open or semi open prisons) up to five years and/or a fine, and only in the case of completed (not attempted) illegal acts.
Cleaning and coding of crime records was completed over a nine-year period from 2013.For each offence, information on the involvement of the cohort member, crime type, and date of crime was manually extracted and coded by two trained law students using all available data (from police, courts, young offender institutions, and prisons), blind to each other's classifications.The cohort member was initially identified as the victim, witness, or offender for each offence, and the crime type was then classified by Brazilian law.For the current study, only records of suspected offenders (not victims or witnesses) were examined.Legal criminal categories were later condensed to create broader crime categories for analyses, including homicide, non-lethal violence, and non-violent crime.The homicide outcome includes all completed and attempted lethal violence, classified in Brazilian law either "homicide" or "latrocínio"a separate lethal offence category meaning homicide followed by theft of property.Both attempted and completed homicides and "latrocínio" offences, are referred to as homicide offences in this article.
The date of crime was taken directly from the records, and where those were occasionally missing, other dates (e.g., date of police investigation or court record) were used to estimate the date of offence, based on average periods between offences and these criminal justice procedures.Since there were often multiple sources of information on the same crime (e.g., from the police and courts), information from the courts were prioritised as these were believed to be a more reliable source.Any classification discrepancies were discussed on a case-by-case basis and resolved by consensus or via the inclusion of a third lawyer.

Details of income data
Monthly income data in the 1982 cohort were collected in interviews and referred to the total amount of household earnings in the month before the interview.The parameter commonly used in Brazil to measure poverty is in terms of Brazilian monthly minimum wages [BMMW; 1].These data were collected with mothers (birth, age 4) and with cohort members (age 22) in continuous level form (expressed in local currency and converted into multiples of the BMMW at the time of the of data collection), except for at birth, when data were originally collected according to five categories of BMMW multiples (≤1.0, >1.0 to 3.0, >3.0 to 6.0, >6.0-10.0 and >10).

Multiple imputation
Imputation models included all study variables.We used multiple imputation by chained equations (MICE) to impute missing data for maternal age (0.02%), maternal education (0.12%), family income at birth (0.46%), family income at 4 years (16.63%),family income at 22 years (24.13%), and violent crime (0.58%), producing 20 datasets with a sample size of 5660, representing all cohort members alive at age 10 years.In line with previous recommendations [2,3], analyses were carried out across the 20 imputed datasets and graphical and internal checks indicated that these imputed data were reasonable.Therefore, additional imputations were not carried out since these would have incurred substantial computational costs (given the subsequent use of bootstrapping on the stacked imputed datasets).

Deviations from the pre-specified analysis plan
The pre-specified analysis plan was carefully followed, with three adjustments.First, we had planned to create a third, additional outcome variable coded 1 for homicide offender, and 0 for non-lethal violent offender.However, recognising that analysing this outcome variable (excluding participants without violent offences) could result in poor estimates because of collider bias, this third outcome variable was not analysed.Second, we did not conduct planned sensitivity analyses restricting the outcome to cases of conviction, because of the low number of offences that resulted in conviction.Third, considering the changing levels of absolute poverty (defined as < BMMW) between birth and early adulthood, we ran an additional sensitivity analysis of relative poverty (defined as bottom third in income distribution at each age).

Findings from sensitivity analyses
Sensitivity analyses identified that the main results were robust to various specifications.Defining poverty as <1 BMMW (compared to <3 BMMW in the main models) also identified early adulthood as a sensitive period for the association between poverty with all violence (Supplementary Table S6; note, homicide analyses could not be conducted robustly given small cell counts for this definition of poverty).Defining poverty in relative terms, as being in the bottom income tertile at each age, also identified early adulthood as a sensitive period for the association between poverty with both all violence and homicide (Supplementary Table S7).
Additionally, restricting official records to violent offending after 22 years old to establish complete temporal precedence (early adulthood poverty occurring before all offending) further supported early adulthood as a sensitive period, yielding stronger evidence of possible causality (Supplementary Table S8).
A sensitivity analysis using self-reported violence (fights) as the outcome also isolated early adulthood as an important exposure period for the influence of poverty (Supplementary Table S8).However, unlike the main models which used official records, there was limited support for cumulative effects of poverty on self-reported violence.Instead, additional specific effects of downward mobility were indicated, where individuals who experienced a shift from no poverty in early childhood to poverty in early adulthood were at increased risk of self-reporting violent outcomes.

Average number of offences and age of offences
Supplementary Table S1.Average number of offences and average age of offences among violent/homicide offenders in the 1982 Pelotas Birth Cohort (n=1151) **Age at first offence also refers to the age at which the specific type of offence (of the offender) was first committed (e.g. the age at first homicide offence among homicide offenders, where median = 22.9).

Poverty exposure and continuity in poverty
Supplementary Table S2 deviation (sd) *Total number offences refers to the specific type of offence for that offender (e.g. the total number of homicide offences committed by each homicide offender; where mean = 1.42).

Table S4 .
Prevalence of poverty at each period of the life-course, and continuity in poverty through time (n=3840) Bivariate associations between poverty through the life-course and violence (imputed analyses, n=5660)Results based on multiple imputation by chained equations (20 imputed datasets) with a sample size of 5660 of those who were still alive at 10 years.†Count of poverty exposure across birth, early childhood, and early adulthood *P <0.05; **P <0.01; ***P <0.001